The SURFEXv7.2 land and ocean surface platform for coupled or offline simulation of Earth surface variables and fluxes

CC Attribution 3.0 License.Final revised paper also available at http://www.geosci-model-dev.net/6/929/2013/gmd-6-929-2013.pdfInternational audienceSURFEX is a new externalized land and ocean surface platform that describes the surface fluxes and the evolution of four types of surface: nature, town, inland water and ocean. It can be run either coupled or in offline mode. It is mostly based on pre-existing, well validated scientific models. It can be used in offline mode (from point scale to global runs) or fully coupled with an atmospheric model. SURFEX is able to simulate fluxes of carbon dioxide, chemical species, continental aerosols, sea salt and snow particles. It also includes a data assimilation module. The main principles of the organization of the surface are described first. Then, a survey is made of the scientific module (including the coupling strategy). Finally the main applications of the code are summarized. The current applications are extremely diverse, ranging from surface monitoring and hydrology to numerical weather prediction and global climate simulations. The validation work undertaken shows that replacing the pre-existing surface models by SURFEX in these applications is usually associated with improved skill, as the numerous scientific developments contained in this community code are used to good advantage

Saharan dust events in the European Alps: role in snowmelt and geochemical characterization

The input of mineral dust from arid regions impacts snow optical properties. The induced albedo reduction generally alters the melting dynamics of the snowpack, resulting in earlier snowmelt. In this paper, we evaluate the impact of dust depositions on the melting dynamics of snowpack at a high-elevation site (2160&thinsp;m) in the European Alps (Torgnon, Aosta Valley, Italy) during three hydrological years (2013–2016). These years were characterized by several Saharan dust events that deposited significant amounts of mineral dust in the European Alps. We quantify the shortening of the snow season due to dust deposition by comparing observed snow depths and those simulated with the Crocus model accounting, or not, for the impact of impurities. The model was run and tested using meteorological data from an automated weather station. We propose the use of repeated digital images for tracking dust deposition and resurfacing in the snowpack. The good agreement between model prediction and digital images allowed us to propose the use of an RGB index (i.e. snow darkening index – SDI) for monitoring dust on snow using images from a digital camera. We also present a geochemical characterization of dust reaching the Alpine chain during spring in 2014. Elements found in dust were classified as a function of their origin and compared with Saharan sources. A strong enrichment in Fe was observed in snow containing Saharan dust. In our case study, the comparison between modelling results and observations showed that impurities deposited in snow anticipated the disappearance of snow up to 38&thinsp;d a out of a total 7 months of typical snow duration. This happened for the season 2015–2016 that was characterized by a strong dust deposition event. During the other seasons considered here (2013–2014 and 2014–2015), the snow melt-out date was 18 and 11&thinsp;d earlier, respectively. We conclude that the effect of the Saharan dust is expected to reduce snow cover duration through the snow-albedo feedback. This process is known to have a series of further hydrological and phenological feedback effects that should be characterized in future research.</p

ESM-SnowMIP: Assessing snow models and quantifying snow-related climate feedbacks

This paper describes ESM-SnowMIP, an international coordinated modelling effort to evaluate current snow schemes, including snow schemes that are included in Earth system models, in a wide variety of settings against local and global observations. The project aims to identify crucial processes and characteristics that need to be improved in snow models in the context of local- and global-scale modelling. A further objective of ESM-SnowMIP is to better quantify snow-related feedbacks in the Earth system. Although it is not part of the sixth phase of the Coupled Model Intercomparison Project (CMIP6), ESM-SnowMIP is tightly linked to the CMIP6-endorsed Land Surface, Snow and Soil Moisture Model Intercomparison (LS3MIP)

The method ADAMONT v1.0 for statistical adjustment of climate projections applicable to energy balance land surface models

We introduce the method ADAMONT v1.0 to adjust and disaggregate daily climate projections from a regional climate model (RCM) using an observational dataset at hourly time resolution. The method uses a refined quantile mapping approach for statistical adjustment and an analogous method for sub-daily disaggregation. The method ultimately produces adjusted hourly time series of temperature, precipitation, wind speed, humidity, and short- and longwave radiation, which can in turn be used to force any energy balance land surface model. While the method is generic and can be employed for any appropriate observation time series, here we focus on the description and evaluation of the method in the French mountainous regions. The observational dataset used here is the SAFRAN meteorological reanalysis, which covers the entire French Alps split into 23 massifs, within which meteorological conditions are provided for several 300 m elevation bands. In order to evaluate the skills of the method itself, it is applied to the ALADIN-Climate v5 RCM using the ERA-Interim reanalysis as boundary conditions, for the time period from 1980 to 2010. Results of the ADAMONT method are compared to the SAFRAN reanalysis itself. Various evaluation criteria are used for temperature and precipitation but also snow depth, which is computed by the SURFEX/ISBA-Crocus model using the meteorological driving data from either the adjusted RCM data or the SAFRAN reanalysis itself. The evaluation addresses in particular the time transferability of the method (using various learning/application time periods), the impact of the RCM grid point selection procedure for each massif/altitude band configuration, and the intervariable consistency of the adjusted meteorological data generated by the method. Results show that the performance of the method is satisfactory, with similar or even better evaluation metrics than alternative methods. However, results for air temperature are generally better than for precipitation. Results in terms of snow depth are satisfactory, which can be viewed as indicating a reasonably good intervariable consistency of the meteorological data produced by the method. In terms of temporal transferability (evaluated over time periods of 15 years only), results depend on the learning period. In terms of RCM grid point selection technique, the use of a complex RCM grid points selection technique, taking into account horizontal but also altitudinal proximity to SAFRAN massif centre points/altitude couples, generally degrades evaluation metrics for high altitudes compared to a simpler grid point selection method based on horizontal distance

The method ADAMONT v1.0 for statistical adjustment of climate projections applicable to energy balance land surface models

We introduce the method ADAMONT v1.0 to adjust and disaggregate daily climate projections from a regional climate model (RCM) using an observational dataset at hourly time resolution. The method uses a refined quantile mapping approach for statistical adjustment and an analogous method for sub-daily disaggregation. The method ultimately produces adjusted hourly time series of temperature, precipitation, wind speed, humidity, and short- and longwave radiation, which can in turn be used to force any energy balance land surface model. While the method is generic and can be employed for any appropriate observation time series, here we focus on the description and evaluation of the method in the French mountainous regions. The observational dataset used here is the SAFRAN meteorological reanalysis, which covers the entire French Alps split into 23 massifs, within which meteorological conditions are provided for several 300 m elevation bands. In order to evaluate the skills of the method itself, it is applied to the ALADIN-Climate v5 RCM using the ERA-Interim reanalysis as boundary conditions, for the time period from 1980 to 2010. Results of the ADAMONT method are compared to the SAFRAN reanalysis itself. Various evaluation criteria are used for temperature and precipitation but also snow depth, which is computed by the SURFEX/ISBA-Crocus model using the meteorological driving data from either the adjusted RCM data or the SAFRAN reanalysis itself. The evaluation addresses in particular the time transferability of the method (using various learning/application time periods), the impact of the RCM grid point selection procedure for each massif/altitude band configuration, and the intervariable consistency of the adjusted meteorological data generated by the method. Results show that the performance of the method is satisfactory, with similar or even better evaluation metrics than alternative methods. However, results for air temperature are generally better than for precipitation. Results in terms of snow depth are satisfactory, which can be viewed as indicating a reasonably good intervariable consistency of the meteorological data produced by the method. In terms of temporal transferability (evaluated over time periods of 15 years only), results depend on the learning period. In terms of RCM grid point selection technique, the use of a complex RCM grid points selection technique, taking into account horizontal but also altitudinal proximity to SAFRAN massif centre points/altitude couples, generally degrades evaluation metrics for high altitudes compared to a simpler grid point selection method based on horizontal distance

Crossing numerical simulations of snow conditions with a spatially-resolved socio-economic database of ski resorts: a proof of concept in the French Alp

International audienceSnow on the ground is a critical resource for winter tourism in mountain regions and in particular ski tourism. Ski resorts are significantly vulnerable to the variability of meteorological conditions already at present and threatened by climate change in the longer term. Here we introduce an approach where detailed snowpack simulation results were crossed with a resort-level geographical and socio-economic database containing information from about 142 ski resorts spanning the entire French Alps domain. This allows us to take into account explicitly the geographical, topographical (altitude, slope and aspect) and spatial organization (distribution of ski-lifts and slopes) features of the ski resorts considered. A natural snow resort viability index was built using all the above information and simulated natural snow conditions from 2000 to 2012. Results were compared to economically relevant information (skier day values) highlighting a complex relationship between ski resort operation and natural snow conditions. The method introduced in this study holds great potential for physically-based and socio-economically-relevant analyses of the functioning of winter tourism economy and projections into the future under climate change conditions. This requires, however, that further improvements are carried out, in particular the explicit integration of snow management practices (e.g. snowmaking and grooming) into the modeling suite

Influence of spatial discretization, underground water storage and glacier melt on a physically-based hydrological model of the Upper Durance River basin

International audienceThe SAFRAN-ISBA-MODCOU hydrological model (Habets et al., 2008) presents severe limitations for alpine catchments. Here we propose possible model adaptations. For the catchment discretization, Relatively Homogeneous Hydrological Units (RHHUs) are used instead of the classical 8 km square grid. They are defined from the dilineation of hydrological subbasins, elevation bands, and aspect classes. Glacierized and non-glacierized areas are also treated separately. In addition, new modules are included in the model for the simulation of glacier melt, and retention of underground water. The improvement resulting from each model modification is analysed for the Upper Durance basin. RHHUs allow the model to better account for the high spatial variability of the hydrological processes (e.g. snow cover). The timing and the intensity of the spring snowmelt floods are significantly improved owing to the representation of water retention by aquifers. Despite the relatively small area covered by glaciers, accounting for glacier melt is necessary for simulating the late summer low flows. The modified model is robust over a long simulation period and it produces a good reproduction of the intra and interannual variability of discharge, which is a necessary condition for its application in a modified climate context

Seasonal evolution of snow permeability under equi-temperature and temperature-gradient conditions

The permeability (<i>K</i>) of snow to air flow affects the transfer of energy, water vapor and chemical species between the snow and the atmosphere. Yet today little is known about the temporal evolution of snow permeability as a function of metamorphic regime. Furthermore, our ability to simulate snow permeability over the seasonal evolution of a snowpack has not been tested. Here we have measured the evolution of snow permeability in a subarctic snowpack subject to high temperature-gradient (TG) metamorphism. We have also measured the evolution of the same snowpack deposited over tables so that it evolved in the equi-temperature (ET) regime. Permeability varies in the range 31 &times; 10^&minus;10 (ET regime) to 650 &times; 10^&minus;10 m² (TG regime). Permeability increases over time in TG conditions and decreases under ET conditions. Using measurements of density <i>&rho;</i> and of specific surface area (SSA), from which the equivalent sphere radius <i>r</i> is determined, we show that the equation linking SSA, density <i>&rho;</i> and permeability, <i>K</i> = 3.0 <i>r</i>² <i>e</i>^{(&minus;0.013 <i>&rho;</i>)} (with <i>K</i> in m², <i>r</i> in m and <i>&rho;</i> in kg m⁻³) obtained in a previous study adequately predicts permeability values. The detailed snowpack model Crocus is used to simulate the physical properties of the TG and ET snowpacks. For the most part, all variables are well reproduced. Simulated permeabilities are up to a factor of two greater than measurements for depth hoar layers, which we attribute to snow microstructure and its aerodynamic properties. Finally, the large difference in permeabilities between ET and TG metamorphic regimes will impact atmosphere-snow energy and mass exchanges. These effects deserve consideration in predicting the effect of climate change on snow properties and snow–atmosphere interactions

Assimilation of surface reflectance in snow simulations: Impact on bulk snow variables

International audienceData assimilation of snow observations significantly improves the accuracy of snow cover simulations. However, remotely-sensed snowpack observations made in areas of complex topography are typically subject to large error and biases, creating a challenge for data assimilation. To improve the reliability of ensemble snowpack simulations, this study investigated the appropriate conditions for assimilating MODIS-like synthetic surface reflectances. We used a simulation system that included the Particle Filter data assimilation technique. More than 270 ensemble simulations involving assimilation of synthetic observations were conducted in a twin experiment procedure for three snow seasons. These tests were aimed at establishing the spectral combination of MODIS-like reflectances that convey the more information in the assimilation system, rendering the most reliable snowpack simulation, and determining the maximum observation errors that the assimilation system could tolerate. The assimilation of the first seven MODIS-like bands, covering visible and near-infrared wavelengths, provided the best scores compared with any other band combination, and thus are highly recommended for use when possible. The simulation system tolerated a maximum deviation from ground truth of 5% without loss of performance. However, the assimilation of the first seven bands of true MODIS surface of reflectance fails on improving simulation results in rouged mountain areas

Snowpack modelling in the Pyrenees driven by kilometric resolution meteorological forecasts

Distributed snowpack simulations in the French and Spanish Pyrenees are carried out using the detailed snowpack model Crocus driven by the numerical weather prediction system AROME at 2.5 km grid spacing, during four consecutive winters from 2010 to 2014. The aim of this study is to assess the benefits of a kilometric-resolution atmospheric forcing to a snowpack model for describing the spatial variability of the seasonal snow cover over a mountain range. The evaluation is performed by comparisons to ground-based measurements of the snow depth, the snow water equivalent and precipitations, to satellite snow cover images and to snowpack simulations driven by the SAFRAN analysis system. Snow depths simulated by AROME–Crocus exhibit an overall positive bias, particularly marked over the first summits near the Atlantic Ocean. The simulation of mesoscale orographic effects by AROME gives a realistic regional snowpack variability, unlike SAFRAN–Crocus. The categorical study of daily snow depth variations gives a differentiated perspective of accumulation and ablation processes. Both models underestimate strong snow accumulations and strong snow depth decreases, which is mainly due to the non-simulated wind-induced erosion, the underestimation of strong melting and an insufficient settling after snowfalls. The problematic assimilation of precipitation gauge measurements is also emphasized, which raises the issue of a need for a dedicated analysis to complement the benefits of AROME kilometric resolution and dynamical behaviour in mountainous terrain